# chatClimate: Grounding Conversational AI in Climate Science

arXiv: https://arxiv.org/abs/2304.05510 · HTML: https://mlsanity.com/paper/2304.05510/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 80 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://mlsanity.com/paper/2009.07118/) (2020)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation](https://mlsanity.com/paper/2302.09210/) (2023)
- [Leveraging Large Language Models for Multiple Choice Question Answering](https://mlsanity.com/paper/2210.12353/) (2022)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [Towards Continual Knowledge Learning of Language Models](https://mlsanity.com/paper/2110.03215/) (2021)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [A Bibliometric Review of Large Language Models Research from 2017 to 2023](https://mlsanity.com/paper/2304.02020/) (2023)
- [DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents](https://mlsanity.com/paper/2303.17071/) (2023)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [Analyzing Sustainability Reports Using Natural Language Processing](https://mlsanity.com/paper/2011.08073/) (2020)

## Built on by (most cited first, 30 of 32)

- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Foundation Models for Spatio-Temporal Data Science: A Tutorial and Survey](https://mlsanity.com/paper/2503.13502/) (2025)
- [Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models](https://mlsanity.com/paper/2402.01749/) (2024)
- [WeatherQA: Can Multimodal Language Models Reason about Severe Weather?](https://mlsanity.com/paper/2406.11217/) (2024)
- [From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility](https://mlsanity.com/paper/2402.16142/) (2024)
- [WildfireGPT: Tailored Large Language Model for Wildfire Analysis](https://mlsanity.com/paper/2402.07877/) (2024)
- [Accelerating Earth Science Discovery via Multi-Agent LLM Systems](https://mlsanity.com/paper/2503.05854/) (2025)
- [ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis](https://mlsanity.com/paper/2406.09838/) (2024)
- [Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation](https://mlsanity.com/paper/2411.00412/) (2024)
- [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023)
- [Climate Change from Large Language Models](https://mlsanity.com/paper/2312.11985/) (2023)
- [ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation](https://mlsanity.com/paper/2412.19819/) (2024)
- [Don't Think Twice! Over-Reasoning Impairs Confidence Calibration](https://mlsanity.com/paper/2508.15050/) (2025)
- [A RAG-Based Multi-Agent LLM System for Natural Hazard Resilience and Adaptation](https://mlsanity.com/paper/2504.17200/) (2025)
- [Automated Fact-Checking of Climate Change Claims with Large Language Models](https://mlsanity.com/paper/2401.12566/) (2024)
- [Artificial Intelligence for Collective Intelligence: A National-Scale Research Strategy](https://mlsanity.com/paper/2411.06211/) (2024)
- [Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals](https://mlsanity.com/paper/2404.13885/) (2024)
- [AI for Extreme Event Modeling and Understanding: Methodologies and Challenges](https://mlsanity.com/paper/2406.20080/) (2024)
- [ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries](https://mlsanity.com/paper/2506.13796/) (2025)
- [ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI](https://mlsanity.com/paper/2602.14135/) (2026)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [Knowledge-Grounded Agentic Large Language Models for Multi-Hazard Understanding from Reconnaissance Reports](https://mlsanity.com/paper/2511.14010/) (2025)
- [Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events](https://mlsanity.com/paper/2411.12880/) (2024)
- [Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models](https://mlsanity.com/paper/2502.15094/) (2025)
- [GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?](https://mlsanity.com/paper/2505.10714/) (2025)
- [ChatSOS: LLM-based knowledge Q&A system for safety engineering](https://mlsanity.com/paper/2312.08629/) (2023)
- [ClimaEmpact: Domain-Aligned Small Language Models and Datasets for Extreme Weather Analytics](https://mlsanity.com/paper/2504.19066/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai and demonstrate its ability to answer challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) chatClimate, and 3) hybrid chatClimate. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.